Ehsan Kashi; Mehri Shahriari
Abstract
News and rumors about the prevalence of corona virus on social media have a significant impact on people. The aim of this study is to examine the topics discussed by people about corona disease in social media from the beginning of corona prevalence to the present day. The research data were collected ...
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News and rumors about the prevalence of corona virus on social media have a significant impact on people. The aim of this study is to examine the topics discussed by people about corona disease in social media from the beginning of corona prevalence to the present day. The research data were collected from people’s comments in posts related to Corona News on Instagram and analyzed using the method of text mining and clustering. Based on the results of the research, the topics of discussion of the citizens were divided into 10 clusters, which are: Lack of sanitary equipment, lack of attention to quarantine, news and rumors, mental condition, information about symptoms, prevention, control and treatment, government and public actions, lack of personal hygiene, death rate in patients and burial, closure of educational activities And economic problems. Then they were compared with the issues in December and January, when some issues such as access to vaccines, hourly traffic restrictions and the mutated virus were added to the concerns of the people, and some of them were addressed by government measures.
Azim Zarei; Mehri Shahriari
Abstract
Customer satisfaction requires the customer to be happy both in daily and long-term and global interactions. People's opinions about the products of a company on websites and social media can provide useful information for companies to evaluate customer satisfaction. In this research, using the methodology ...
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Customer satisfaction requires the customer to be happy both in daily and long-term and global interactions. People's opinions about the products of a company on websites and social media can provide useful information for companies to evaluate customer satisfaction. In this research, using the methodology text mining and k- means clustering, customers' opinions about the three brands of Snowa, Pakshoma and Parskhazar from domestic appliances and comments on the three brands of Samsung, LG, and Tefal from external home appliances in the website of Digikala.com were analyzed. The results of this study show that dissatisfaction factors were clustered in six attributes, product failure, and price proportions with performance, efficiency, design, manufacturing quality and after-sales services. In domestic appliances, the most dissatisfaction factors were the product failure, price proportions with performance, manufacturing quality, after-sales service, efficiency, and design. And the factors causing dissatisfaction in external home appliances were manufacturing quality, product failure, design, after-sales service, price proportions with performance, and efficiency.
Sina Raeesi Vanani; Iman Raeesi Vanani; Mohammad Taghi Taghavifard
Abstract
Educational performance measurement through the identification and analysis of data extracted from learners’ activities can effectively result in the improvement of educational performance. In this Article, data of international learners was analyzed based on design science methodology and using ...
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Educational performance measurement through the identification and analysis of data extracted from learners’ activities can effectively result in the improvement of educational performance. In this Article, data of international learners was analyzed based on design science methodology and using data mining methods. In this regard, domestic and international research has been reviewed over the past decade and the academic and non-academic data of students were clustered into three categories: family, supportive, and academic behavior. After the validation of algorithms outputs and determining the number of optimal clusters in each category, clusters were labeled and analyzed. Analysis of labels presents the experience of success or failure of students and roots of effective performance in each cluster, and the labeling method proposed is a new and applicable method in most of the learning centers for segmenting and formulating the educational performance.
Mohamammad Ali KhatamiFirouzabadi; MohammadTaghi TaghaviFard; Khalil Sajjadi; Jahanyar Bamdad Soufi
Abstract
Knowing customer behavior patterns, clustering and providing proper services to the customers is one of the most important issues for the banks.In this research, 5 criteria of each customer, including Recency, Frequency, Monetary, Loan and Deferred, were extracted from a bank database during a fiscal ...
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Knowing customer behavior patterns, clustering and providing proper services to the customers is one of the most important issues for the banks.In this research, 5 criteria of each customer, including Recency, Frequency, Monetary, Loan and Deferred, were extracted from a bank database during a fiscal year, and then customers were clustered using K-Means algorithm. Then, a multi-objective model of bank service allocation was designed for each of the clusters. The purpose of the designed model was to increase customer satisfaction, reduce costs, and reduce the risk of allocating services. Given the fact that the problem does not have an optimal solution, and each client feature has a probability distribution function, simulation was used to solve the models. To determine the optimal solution, Simulated Annealing algorithm was used to create neighboring solutions and consequently a simulation model was implemented. The results showed a significant improvement in the current situation. In this research, we used Weka and R-Studio software for data mining and Arena for simulation and optimization
Mohammad Reza Taghva; Mohammad Mehrabioun Mohammadi; Ahad Zare Ravasan; Amir Arzi Soltan
Abstract
This research aims at associating two groups of demographic and transaction related factors and furthermore, proposes customer churn factor as another influential factor in customer value analysis. To this end, at first, customers' transaction data in a real local ISP in a four year period are utilized ...
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This research aims at associating two groups of demographic and transaction related factors and furthermore, proposes customer churn factor as another influential factor in customer value analysis. To this end, at first, customers' transaction data in a real local ISP in a four year period are utilized for segmentation purpose using k-means method. Regarding service nature of the case, customers' behavior has been considered in terms of customer satisfaction factors. The results of Exploratory Factor Analysis (EFA) indicate that three factors of service quality, expectation confirmation and post-purchasing satisfaction are influential factors. Then, association rules using GRI algorithm are exploited in order to investigate among customers' behaviors and propose appropriate strategies and action plans for each customer segment. Finally, segmentation results are associated with physiographic variables and existence of a significant difference among identified segments is more investigated using one way ANOVA test. The results clarified that expectation confirmation and post- purchasing satisfaction as hygienic factors and service quality as an incentive factor influence customer loyalty.
Mohammad’reza Gholamian; Azimeh Mozafari
Abstract
Management and evaluation of valuable customers, is one of the most important banking factors to reduce costs and increase profitability. In recent decades, many researchers have studied on the analysis of the customer attributes to evaluate value of them using data mining techniques and decision tree ...
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Management and evaluation of valuable customers, is one of the most important banking factors to reduce costs and increase profitability. In recent decades, many researchers have studied on the analysis of the customer attributes to evaluate value of them using data mining techniques and decision tree is one of the most widely used data mining algorithms in the field. Since this algorithm for built tree, considers only one attribute at a time to test each node and ignores the dependency between attributes, therefore, required maximum memory is increased. To solve this problem, in this research a method is proposed to improve the decision tree using neural network to explore the dependency between the attributes based on reduction in required maximum memory that is used based on RFM model to predict customer values. Results show that the proposed method using dependencies between attributes will predict the new customer values by less maximum memory compare to the basic method
